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Spectrograms – audio signal processing for machine learning
- l33tman 5y agoJust noting that this is long and incredibly basic. It doesn't cover anything about spectrograms for example despite the subject title.
- ganzuul 5y agoThere needs to be experiments with representations such as chirplets since those are actually physically relevant.
- deleted 5y ago[deleted]
- stereosteve 5y agoThe Audio Signal Processing for Machine Learning [1] youtube playlist is a pretty excellent course on sound processing for ML. In particular, the MFCC video. [2] [1] https://www.youtube.com/watch?v=iCwMQJnKk2c&list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0 https://www.youtube.com/watch?v=iCwMQJnKk2c&list=PL-wATfeyAM... [2] https://www.youtube.com/watch?v=4_SH2nfbQZ8 https://www.youtube.com/watch?v=4_SH2nfbQZ8
- TrackerFF 5y agoFWIW, if you want to dip your toes into audio and song classification, the Spotify API offers feature extraction. Also, there are some very cool ML topics in the frequency domain. For example - in the past years there's been a small revolution in the guitar / bass / etc. industry where simulators offer quite realistic sounding amps/speakers sounds. Back in the day, these simulators were just carefully crafted filters that would emulate the response of said amps, speakers, room/ambience, etc. but with the advent of more powerful DSP, it is now possible to sort of "extract" these features on the fly - some of these products do indeed use ML/DL algorithms in their core. In the end, the goal is to profile the environment, and create some complex filter / function that matches the response. Which is one thing Neural Networks can do quite well (approximating functions).